My responses are in brackets below, plus a final note after the main text.
----- Original Message -----
From: Uwe Ligges <lig...@statistik.tu-dortmund.de>
To: Scott Raynaud <scott.rayn...@yahoo.com>
Cc: "r-help@r-project.org" <r-help@r-project.org>
Sent: Thursday, November 17, 2011 9:16 AM
Subject: Re: [R] modelling and R misconceptions; was: package installtion
This is hopeless [That's a matter of perception-even concentration camp
prisoners
found a way to hope (see Viktor Frankl)], since you never [never is a strong
word
and many times leads to cognitive errors] seem to listen to our
advice [It's possible that I misunderstood your recommedations (more likely),
or that you communicated poorly (less likely)],
therefore this will be my very last try:
So you actually need local advice [Yes I need advice-that's why I post here!],
both for statistical concepts and R related [I don't claim to be a statistical
genius,
but I can hold my own. Now, R is a different matter]. No statistics software
can estimate effects of variables that you observed to be constant (e.g. 0)
all the time [I think you misuderstood my intentions-I never wanted to estimate
effects that are 0 all of the time]. If any software does,
please delete it a once from your machine.
Instead, ask a local statistician for advice on your problem. You
certainly want to show the data and your model to the local expert -
since you don't show us. [I gave a detailed explanation in a previous post
which I repeat here:
|OK, I'm using William Browne's MLPowSim to create an R script which will
simulate samples for estimation of sample size in mixed models. I have subjects
| nested in hospitals with hospitals treated as random and all of my covariates
at level 1. My outcome is death, so it's binary and I'll have a fixed and
|random intercept. My interest is in the relation of the covariates to the
outcome.
|
|My most important variable is gestational age (GA) which my investigators
divide thusly: 23-24, 25-26, 27-28, 29-30 and 31-32. I have recoded the
| dummies for GA in the script according to the MLPowSim instructions to a
random multinomial variable:
|
| macpred<-rmultinom(n2,1,c(.1031,.1482,.2385,.4404,.0698))
| x[,3]<-macpred[1,][l2id]
| x[,4]<-macpred[2,][l2id]
| x[,5]<-macpred[3,][l2id]
| x[,6]<-macpred[4,][l2id]
|
|GA 23-24 is the reference with p=.0698. I started with a structured sampling
scheme of 20, 60, 100, 120 and 140 level 2 units. My level 2 units have
|different sizes. So at 20 I had 5 hospitals with 100 patients, 4 with 280, 3
with 460, 3 with 640, 3 with 820 and 2 with 1000. Thus, at 60 hospitals, I
have 15,
|12, 9, 9, 9, 6 with the same cell sample sizes.
|
|According to the MLPowSim documentation, with small probablities it's possible
to have a column of zeroes in the X matrix if there are not many units in
|the random factor. R will choke on this but MLWin sets the associated fixed
effects to 0. When R choked, I increased from 20 to 60 as my minimum as
|suggested in the MLPowSim documentation. Still no luck.
Since this is a simulation, I assume once and a while that by chance a
coefficient could be 0.
In fact, Browne mentions as much in his documentation. There is a bit more to
my simulation,
but I thought I'd try to keep it as simple as possible, at least at the outset.]
And then you want to ask for local R course
since reading the documentation seems not to help [You got that right!].
Applying mtrace() in
a non exiting object shows this straight away.
Uwe Ligges
Apparently I misuderstood the prupose of mtrace after reading the
documentation-I thought it was
to debug problems of the sort I've encountered. Michael Weylandt provided
appropriate direction
in the previous post for which I am grateful.
Not all of us can be intellectual superstars. That's why we ask for help.
This much I did read and understand
from the R posting guide:
Responding to other posts:
* Rudeness and ad hominem comments are not acceptable. Brevity is OK.
It's a good lesson to learn.
On 17.11.2011 15:49, Scott Raynaud wrote:
> I believe the problem is a column of zeroes in my x matrix. I have tried the
> suggestions in the documentation,
> so now to try to confirm the probelm I'd like to run debug. Here's where I
> think the problem is:
>
> ###~~~~~~~~~~ Fitting the model using lmer funtion ~~~~~~~~~~###
> (fitmodel<- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1))
> mtrace(fitmodel)
>
> I added the mtrace to catch the error, but get the following:
>
> Error in mtrace(fitmodel) : Can't find fitmodel
>
> How can I debug this?
>
>
> ----- Original Message -----
> From: Rolf Turner<rolf.tur...@xtra.co.nz>
> To: Scott Raynaud<scott.rayn...@yahoo.com>
> Cc: "r-help@r-project.org"<r-help@r-project.org>
> Sent: Wednesday, November 16, 2011 6:04 PM
> Subject: Re: [R] package installtion
>
> On 17/11/11 05:37, Scott Raynaud wrote:
>> That might be an option if it weren't my most important predictor. I'm
>> thinking my best bet is to use MLWin for the estimation since it will
>> properly set fixed effects
>> to 0. All my other sample size simulation programs use SAS PROC IML
>>which I don't have/can't afford. I like R since it's free, but I can't work
>>around the problem
>> I'm currently having.
>
> This is the ``push every possible button until you get a result and to hell
> with what
> anything actually means'' approach to statistics. The probability of getting
> a
> *meaningful* result from this approach is close to zero.
>
> Why don't you try to *understand* what is going on, rather than wildly
> throwing
> every possible piece of software at the problem until one such piece runs?
>
> cheers,
>
> Rolf Turner
>
>
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